Maximum Likelihood Estimation and Inference: With examples in R, SAS and ADMB

As the first textbook on ADMB, this book is highly expected.

Where to find good data sets

This post provides some good information on where to find good data sets for statistical analysis.

Programming languages on the rise

This post list seven programming languages that are rising, from Python to Ruby to R to... Cobol.

Very interesting blog

That is a very interesting blog on R + LaTeX + Sweave!

Comparison of results

I am doing a simple comparison of different estimation procedures in dealing with a simple binomial model. Here is where I got started:
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library(INLA)
library(npmlreg)
library(MCMCglmm)
library(DPpackage)


data(Seeds)


# Using INLA
formula = r ~ x1*x2 + f(plate, model="iid")
mod.inla = inla(formula, data=Seeds, family="binomial", Ntrials=n)
summary(mod.seeds)


# Using npmlreg
mod.ml <- alldist(cbind(r, n-r) ~ x1*x2 , random=~1, data=Seeds, family=binomial, random.distribution="gq")
summary(mod.ml)


# Using MCMCglmm
prior <- list(R=list(V=1, nu=0.002))
mod.mcmc <- MCMCglmm(cbind(r, n-r) ~ x1*x2, family="multinomial2", data=Seeds, prior=prior)
summary(mod.mcmc$Sol)


# Using DPpackage

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I will keep updating by adding new things (estimation procedures, predictive simulations, etc.)

Eee Note

Asus's Eee Note looks interesting.